The effect of Topical Application of Extra Virgin Olive Oil on Alleviating Knee Pain in Patients with Knee Osteoarthritis: a Pilot Study
Bibliographic record
Abstract
Objective: To investigate and compare the effect of topical application of extra virgin olive oil (EVOO) on the pain of osteoarthritic knee (s) with the effect of non-steroidal anti-inflammatory drugs. Participants: A convenient sample of thirty patients with knee(s) osteoarthritis (16 females and 14 males) were randomly assigned to three groups; Group A (n=10) treated with topical application of EVOO and exercise, group B (n=10) received topical non-steroidal anti-inflammatory drugs (Ketoprofen gel) over the knee(s) and exercise, and group C received therapeutic exercise only. Method: Treatment was carried out in physical therapy outpatient clinic for 5 days a week for two weeks. Group A received topical EVOO (3ml) and group B received topical ketoprofen gel (3 cm2) three times a day followed by therapeutic exercise. Group C received therapeutic exercise only three times a day. Pain visual analog scale (VAS) and Western Ontario and McMaster (WOMC) universities index measurement were taken at baseline and after two weeks of treatment. Results: All groups showed significant improvement (p0.05), however, both groups significantly experienced less pain than group C (p Conclusion: Topical application of EVOO showed to be effective in alleviating the symptoms of patients diagnosed with knee osteoarthritis compared to topical application of NSAID.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".